Research on Recommendation Method Based on Sentimental Attention of Review Text

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Research on Recommendation Method Based on Sentimental Attention of Review Text

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[1]ZHENG Jianxing,GUO Tongtong,SHEN Lihua,et al.Research on Recommendation Method Based on Sentimental Attention of Review Text[J].Journal of Zhengzhou University (Engineering Science),2022,43(02):44-50.[doi:10.13705/j.issn.1671-6833.2022.02.007]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 43 Number of periods: 2022 02 Page number: 44-50 Column: Public date: 2022-02-27

Title:: Research on Recommendation Method Based on Sentimental Attention of Review Text

Author(s):: ZHENG Jianxing¹; GUO Tongtong¹; SHEN Lihua²; LI Deyu¹; 1.College of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;
2.Shanxi Information Industry Technology Research Institute Co., Ltd., Taiyuan 030012, China

Keywords:: review text; sentimental features; attention mechanism; recommendation

CLC:: TP301

DOI:: 10.13705/j.issn.1671-6833.2022.02.007

Abstract:: Recommendation methods of deep learning-based on review text mainly means to describe the feature information of users and items by terms of review texts, by rating relationship between users and items to improve the recommendation performance. Existing studies ignore the interpretable contribution of sentimental features on the rating prediction. To solve this problem, by incorporating the roles of review text and sentimental polarity orientation in the embeddings of users and items, respectively, a sentimental attention recommendation method was proposed based on review text (IncorRAS-Rec); Firstly, CNN (convolutional neural network) was used to handle review sets for users and items, represent the review features of users and items, and obtain relevant users features and items features; Then, by combining users′ rating preference for items, users and items embedding with reviews′ sentimental features were learned. Secondly, by aggregating reviews′ relevant sentimental feature information for users and items in terms of attention mechanism, the embeddings of users and items were learned; Finally, the users ratings on the items were predicted based on users and items embeddings together with their bias information. The experimental comparison and analysis were carried out on public Amazon datasets, to evaluates the effectiveness of the model performance. Experimental results showed that the proposed IncorRAS-Rec model not only could outperform other traditional methods in terms of RMSE(Root mean square error) and MAE(Mean absolute error) metrics, but also implement the explanatory role of sentimental features in rating prediction.

References:: ［1］ KOＲEN Y，BELL Ｒ，VOLINSKY C．Matrix factorization techniques for recommender systems［J］． Computer， 2009，42( 8) : 30－37．
［2］柴玉梅，员武莲，王黎明，等．基于双注意力机制和迁移学习的跨领域推荐模型［J］．计算机学报， 2020，43( 10) : 1924－1942．
［3］ ZHENG L，NOＲOOZI V，YU P S．Joint deep modeling of users and items using reviews for recommendation ［C］/ /Proceedings of the Tenth ACM International Conference on Web Search and Data Mining． Cambridge United Kingdom． New York: ACM，2017: 425－434．
［4］ CHEN C，ZHANG M，LIU Y Q，et al．Neural attentional rating regression with review-level explanations［C］/ / Proceedings of the 2018 World Wide Web Conference on World Wide Web． New York: ACM，2018: 1583 －1592．
［5］李勇，金庆雨，张青川．融合位置注意力机制和改进 BLSTM 的食品评论情感分析［J］．郑州大学学报( 工学版) ，2020，41( 1) : 58－62．
［6］ FAN W Q，MA Y，LI Q，et al．Graph neural networks for social recommendation［C］/ /The World Wide Web Conference．New York: ACM，2019: 417－426．
［7］ＲENDLE S． Factorization machines［C］/ /2010 IEEE International Conference on Data Mining． Piscataway: IEEE，2010: 995－1000．
［8］ HE X N，CHUA T S．Neural factorization machines for sparse predictive analytics［C］/ /Proceedings of the 40th International ACM SIGIＲ Conference on Ｒesearch and Development in Information Ｒetrieval．New York: ACM，2017: 355－364．
［9］ XIN X，CHEN B，HE X N，et al．CFM: convolutional factorization machines for context-aware recommendation ［C］/ /Proceedings of the 28th International Joint Conference on Artificial Intelligence( IJCAI 2019) ．New York: ACM，2019: 3926－3932．
［10］ GUO W，ZHANG C，GUO H F，et al．Multi-branch convolutional network for context-aware recommendation［C］/ / Proceedings of the 43rd International ACM SIGIＲ Conference on Ｒesearch and Development in Information Ｒetrieval．New York: ACM，2020: 1709－1712．
［11］ WU L B，QUAN C，LI C L，et al．A context-aware useritem representation learning for item recommendation ［J］．ACM transactions on information systems，2019， 37( 2) : 1－29．
［12］ CHIN J Y，ZHAO K Q，JOTY S，et al． ANＲ: aspectbased neural recommender［C］/ /Proceedings of the 27th ACM International Conference on Information and Knowledge Management． New York: ACM，2018: 147 －156．
［13］ TAY Y，LUU A T，HUI S C．Multi-pointer co-attention networks for recommendation［C］/ /Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining．New York: ACM， 2018: 2309－2318．
［14］冯兴杰，曾云泽．基于评分矩阵与评论文本的深度推荐模型［J］．计算机学报，2020，43( 5) : 884－900．
［15］ MNIH A，SALAKHUTDINOV Ｒ． Probabilistic matrix factorization ［ C ］/ /Proceedings of the 20th International Conference on Neural Information Processing Systems． New York: ACM，2007: 1257－1264．
［16］ SALAKHUTDINOV Ｒ，MNIH A．Bayesian probabilistic matrix factorization using Markov chain Monte Carlo ［C ］/ /Proceedings of the 25th International Conference On Machine Learning． New York: ACM， 2008: 880－887．
［17］ KOＲEN Y． Factorization meets the neighborhood: a multifaceted collaborative filtering model［C］/ /Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining．New York: ACM，2008: 426－434．

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Last Update: 2022-02-25